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Long-horizon reasoning models can now achieve $O(1)$ memory and $O(N)$ time complexity while surpassing larger Transformer baselines on complex tasks.
Unlearning sensitive information in LLMs can be achieved without sacrificing performance, with ADU preserving up to 98% of model utility while effectively forgetting.
